Serverless Data Processing Using Azure Tools

Serverless Data Processing Using Azure Tools

In this blog, we will see it in action using an example. See how to combine real-time data ingestion component with a Serverless processing layer.

One of the previous blogs covered some of the concepts behind how Azure Event Hubs supports multiple protocols for data exchange. In this blog, we will see it in action using an example. With the help of a sample app, you will see how to combine real-time data ingestion component with a Serverless processing layer.

The sample application has the following components:

To follow along and deploy this solution to Azure, you are going to need a Microsoft Azure account. You can grab one for free if you don't have it already!

Application Components

Let's go through the individual components of the applications

As always, the **[code is available on GitHub](**

Producer Component

This is pretty straightforward - it is a Go app which uses the Sarama Kafka client to send (simulated) "orders" to Azure Event Hubs (Kafka topic). It is available in the form of a Docker image for ease of use (details in next section)

Here is the relevant code snippet:



order := Order{OrderID: "order-1234", CustomerID: "customer-1234", Product: "product-1234"}



b, err := json.Marshal(order)



msg := &sarama.ProducerMessage{Topic: eventHubsTopic, Key: sarama.StringEncoder(oid), Value: sarama.ByteEncoder(b)}



A lot of the details have been omitted (from the above snippet) - you can grok through the full code here. To summarize, an Order is created, converted (marshaled) into JSON (bytes) and sent to Event Hubs Kafka endpoint.

Serverless Component

The Serverless part is a Java Azure Function. It leverages the following capabilities:

The Trigger allows the Azure Functions logic to get invoked whenever an order event is sent to Azure Event Hubs. The Output Binding takes care of all the heavy lifting such as establishing database connection, scaling, concurrency, etc. and all that's left for us to build is the business logic, which in this case has been kept pretty simple - on receiving the order data from Azure Event Hubs, the function enriches it with additional info (customer and product name in this case), and persists it in an Azure Cosmos DB container.

You can check the [OrderProcessor]( code on Github, but here is the gist:


public void storeOrders(
  @EventHubTrigger(name = "orders", eventHubName = "", connection = 
  "EventHubConnectionString", cardinality = Cardinality.ONE) 
  OrderEvent orderEvent,
  @CosmosDBOutput(name = "databaseOutput", databaseName = "AppStore", 
  collectionName = "orders", connectionStringSetting = 
  OutputBinding<Order> output,
  final ExecutionContext context) {
Order order = new Order(orderEvent.getOrderId(),Data.CUSTOMER_DATA.get(orderEvent.getCustomerId()), orderEvent.getCustomerId(),Data.PRODUCT_DATA.get(orderEvent.getProduct());

The storeOrders method is annotated with [@FunctionName]( and it receives data from Event Hubs in the form of an OrderEvent object. Thanks to the [@EventHubTrigger]( annotation, the platform that takes care of converting the Event Hub payload to a Java POJO (of the type OrderEvent) and routing it correctly. The connection = "EventHubConnectionString" part specifies that the Event Hubs connection string is available in the function configuration/settings named EventHubConnectionString

The [@CosmosDBOutput]( annotation is used to persist data in Azure Cosmos DB. It contains the Cosmos DB database and container name, along with the connection string which will be picked up from the CosmosDBConnectionString configuration parameter in the function. The POJO (Order in this case) is persisted to Cosmos DB with a single setValue method call on the [OutputBinding]( object - the platform makes it really easy, but there is a lot going on behind the scenes!

Let's switch gears and learn how to deploy the solution to Azure

cloud tutorial azure serverless databases messaging azure functions azure cosmos db

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